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Trentino Dataset (RMAWGEN)

Trentino daily climate dataset by RMAWGEN R package (https://CRAN.R-project.org/package=RMAWGEN E. and E. (2016)) contains all information about meteorological stations and instrumental timeseries in 59 sites located in Trentino (Italy) and its neighborood. In particular, it contains daily precipitantion, maximimum and minimum temperature. Original data are provided by Provincia Autonoma di Trento (https://www.meteotrentino.it/), Fondazione Edmund Mach (https://www.fmach.it), Provincia Autonama di Bolzano/Autome Provinz Bozen (http://www.provincia.bz.it/meteo), ARPA Lombardia (https://www.arpalombardia.it/), ARPA Veneto (https://www.arpa.veneto.it/previsioni/it/html/index.php). Two more stations (WWRLUN and WWTRES) provided by the present project are added.

Available Projections (from CORDEX)

The trentino + 2 more stations dataset station locations are inserted within the grid provided by CORDEX outcome files (https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview).

List of RCM and GCM models

Here is a list of available combinations of Global Climate Models (GCMs) and Regional Climate Models RCMS Provided by EURO-CORDEX project provided by Copenicus Data Storage in collaboration with ECMWF (https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=doc,https://confluence.ecmwf.int/display/CKB/CORDEX%3A+Regional+climate+projections, https://climate.copernicus.eu/sites/default/files/2021-03/C3SWebinar1_ErikKjellstrom.pdf )(e.g. Coppola et al. (2021), Vautard et al. (2021), Christensen and Kjellström (2020) https://link.springer.com/article/10.1007/s00382-020-05229-y,).

GCMs:

RCMS:

  • CLMcom-CCLM4-8-17
  • GERICS-REMO2015
  • CLMcom-ETH-COSMO-crCLIM-v1-1
  • DMI-HIRHAM5
  • KNMI-RACMO22E
  • SMHI-RCA4
  • UHOH-WRF361H
  • CNRM-ALADIN63
  • ICTP-RegCM4-6
  • MOHC-HadREM3-GA7-05
  • MPI-CSC-REMO2009

Climate predictions with GCMs are subsequently downscaled at about-10 km resolutions by RCMs.

Monthly Time Series 1951-2005 (historical); 2006-2100 (RCP85)

Averaged daily maximum and minimum temperature

Extracted monthly predicted time series cover the area of grid cells covering in which the station site is contained. Gridded model values from RCMs are subsequently statistical downscaled and then compared with observations, when available, for the historical period (1951-2005) and the RCP8.5 scenario (https://en.wikipedia.org/wiki/Representative_Concentration_Pathway)(2006-2100). Downscaling from the RCM value to the site-scale value has been performed with an ARIMA model. Model are calibarated for the first 10 years of the time series of observations at WWRLUN and WWTRES. Observation at WWRLUN and WWTRES are avaiable from 2001 to 2022 (March). Downscaled time series with the best fit and tat can be accepted for further analysis are visualized. Scrolling visulalization are only available in the HTML of this report.

Godness of fit metrics is based on RMSE (Root Mean Square Error, https://en.wikipedia.org/wiki/Root-mean-square_deviation), MAE (https://en.wikipedia.org/wiki/Mean_absolute_error) and KGE (Kling-Gupta efficiency, see “kge” function on hydroGOF R package Mauricio Zambrano-Bigiarini (2020)):

station variable model RMSE MAE KGE
WWRLUN tasmin observation 0.00 0.00 1.00
WWRLUN tasmin MPI-M-MPI-ESM-LR_CLMcom-CCLM4-8-17 2.66 2.08 0.87
WWRLUN tasmin MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 2.65 2.06 0.91
WWRLUN tasmin MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.50 1.97 0.88
WWRLUN tasmin MPI-M-MPI-ESM-LR_KNMI-RACMO22E 2.81 2.20 0.87
WWRLUN tasmin MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.42 1.89 0.90
WWRLUN tasmin MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009 2.93 2.22 0.83
WWRLUN tasmin MPI-M-MPI-ESM-LR_SMHI-RCA4 3.13 2.38 0.79
WWRLUN tasmin MPI-M-MPI-ESM-LR_UHOH-WRF361H 7.00 5.82 0.00
WWRLUN tasmax observation 0.00 0.00 1.00
WWRLUN tasmax MPI-M-MPI-ESM-LR_CLMcom-CCLM4-8-17 3.46 2.77 0.83
WWRLUN tasmax MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 3.18 2.52 0.86
WWRLUN tasmax MPI-M-MPI-ESM-LR_DMI-HIRHAM5 3.41 2.71 0.81
WWRLUN tasmax MPI-M-MPI-ESM-LR_KNMI-RACMO22E 4.40 3.48 0.74
WWRLUN tasmax MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 3.24 2.58 0.87
WWRLUN tasmax MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009 4.58 3.68 0.69
WWRLUN tasmax MPI-M-MPI-ESM-LR_SMHI-RCA4 4.45 3.46 0.77
WWRLUN tasmax MPI-M-MPI-ESM-LR_UHOH-WRF361H 7.42 5.93 0.32
WWRLUN tasmin MOHC-HadGEM2-ES_CLMcom-CCLM4-8-17 7.18 5.89 -0.05
WWRLUN tasmin MOHC-HadGEM2-ES_CLMcom-ETH-COSMO-crCLIM-v1-1 7.47 6.27 -0.18
WWRLUN tasmin MOHC-HadGEM2-ES_DMI-HIRHAM5 7.16 5.95 -0.06
WWRLUN tasmin MOHC-HadGEM2-ES_GERICS-REMO2015 6.95 5.66 0.03
WWRLUN tasmin MOHC-HadGEM2-ES_KNMI-RACMO22E 7.42 6.14 -0.12
WWRLUN tasmin MOHC-HadGEM2-ES_MOHC-HadREM3-GA7-05 6.25 5.22 0.13
WWRLUN tasmin MOHC-HadGEM2-ES_SMHI-RCA4 7.45 6.14 -0.12
WWRLUN tasmin MOHC-HadGEM2-ES_UHOH-WRF361H 7.49 6.28 -0.17
WWRLUN tasmax MOHC-HadGEM2-ES_CLMcom-CCLM4-8-17 10.37 8.27 -0.11
WWRLUN tasmax MOHC-HadGEM2-ES_CLMcom-ETH-COSMO-crCLIM-v1-1 10.64 8.51 -0.64
WWRLUN tasmax MOHC-HadGEM2-ES_DMI-HIRHAM5 10.67 8.54 -0.83
WWRLUN tasmax MOHC-HadGEM2-ES_GERICS-REMO2015 10.21 8.14 -0.06
WWRLUN tasmax MOHC-HadGEM2-ES_KNMI-RACMO22E 10.71 8.65 -0.82
WWRLUN tasmax MOHC-HadGEM2-ES_MOHC-HadREM3-GA7-05 9.89 7.97 -0.02
WWRLUN tasmax MOHC-HadGEM2-ES_SMHI-RCA4 10.29 8.15 -0.07
WWRLUN tasmax MOHC-HadGEM2-ES_UHOH-WRF361H 10.64 8.52 -0.79
WWRLUN tasmin MIROC-MIROC5_CLMcom-CCLM4-8-17 3.45 2.73 0.81
WWRLUN tasmin MIROC-MIROC5_GERICS-REMO2015 2.72 2.14 0.80
WWRLUN tasmax MIROC-MIROC5_CLMcom-CCLM4-8-17 4.12 3.21 0.84
WWRLUN tasmax MIROC-MIROC5_GERICS-REMO2015 3.71 2.81 0.83
WWRLUN tasmin ICHEC-EC-EARTH_CLMcom-ETH-COSMO-crCLIM-v1-1 2.77 2.21 0.79
WWRLUN tasmin ICHEC-EC-EARTH_DMI-HIRHAM5 2.61 2.02 0.83
WWRLUN tasmin ICHEC-EC-EARTH_KNMI-RACMO22E 3.26 2.56 0.68
WWRLUN tasmin ICHEC-EC-EARTH_SMHI-RCA4 3.02 2.35 0.77
WWRLUN tasmax ICHEC-EC-EARTH_CLMcom-ETH-COSMO-crCLIM-v1-1 2.88 2.31 0.86
WWRLUN tasmax ICHEC-EC-EARTH_DMI-HIRHAM5 2.79 2.15 0.87
WWRLUN tasmax ICHEC-EC-EARTH_KNMI-RACMO22E 3.07 2.35 0.88
WWRLUN tasmax ICHEC-EC-EARTH_SMHI-RCA4 3.66 2.89 0.81
WWRLUN tasmin CCCma-CanESM2_CLMcom-CCLM4-8-17 2.93 2.32 0.90
WWRLUN tasmin CCCma-CanESM2_GERICS-REMO2015 2.28 1.72 0.92
WWRLUN tasmax CCCma-CanESM2_CLMcom-CCLM4-8-17 3.30 2.64 0.90
WWRLUN tasmax CCCma-CanESM2_GERICS-REMO2015 2.84 2.19 0.93
WWTRES tasmin observation 0.00 0.00 1.00
WWTRES tasmin MPI-M-MPI-ESM-LR_CLMcom-CCLM4-8-17 2.40 1.90 0.87
WWTRES tasmin MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 2.26 1.74 0.91
WWTRES tasmin MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.16 1.70 0.90
WWTRES tasmin MPI-M-MPI-ESM-LR_KNMI-RACMO22E 2.64 2.17 0.81
WWTRES tasmin MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.14 1.66 0.93
WWTRES tasmin MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009 2.32 1.79 0.88
WWTRES tasmin MPI-M-MPI-ESM-LR_SMHI-RCA4 2.52 1.94 0.89
WWTRES tasmin MPI-M-MPI-ESM-LR_UHOH-WRF361H 3.37 2.83 0.64
WWTRES tasmax observation 0.00 0.00 1.00
WWTRES tasmax MPI-M-MPI-ESM-LR_CLMcom-CCLM4-8-17 4.65 3.87 0.75
WWTRES tasmax MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 3.91 3.13 0.80
WWTRES tasmax MPI-M-MPI-ESM-LR_DMI-HIRHAM5 3.44 2.77 0.81
WWTRES tasmax MPI-M-MPI-ESM-LR_KNMI-RACMO22E 6.16 5.28 0.62
WWTRES tasmax MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 3.54 2.82 0.83
WWTRES tasmax MPI-M-MPI-ESM-LR_MPI-CSC-REMO2009 5.21 4.34 0.68
WWTRES tasmax MPI-M-MPI-ESM-LR_SMHI-RCA4 3.48 2.76 0.83
WWTRES tasmax MPI-M-MPI-ESM-LR_UHOH-WRF361H 8.52 7.42 0.39
WWTRES tasmin MOHC-HadGEM2-ES_CLMcom-CCLM4-8-17 11.74 9.80 -0.96
WWTRES tasmin MOHC-HadGEM2-ES_CLMcom-ETH-COSMO-crCLIM-v1-1 12.02 9.99 -1.45
WWTRES tasmin MOHC-HadGEM2-ES_DMI-HIRHAM5 11.90 9.92 -1.34
WWTRES tasmin MOHC-HadGEM2-ES_GERICS-REMO2015 11.27 9.40 -0.86
WWTRES tasmin MOHC-HadGEM2-ES_KNMI-RACMO22E 11.87 9.89 -0.99
WWTRES tasmin MOHC-HadGEM2-ES_MOHC-HadREM3-GA7-05 11.26 9.46 -0.86
WWTRES tasmin MOHC-HadGEM2-ES_SMHI-RCA4 11.56 9.66 -0.92
WWTRES tasmin MOHC-HadGEM2-ES_UHOH-WRF361H 11.92 9.93 -1.47
WWTRES tasmax MOHC-HadGEM2-ES_CLMcom-CCLM4-8-17 15.51 13.38 -0.32
WWTRES tasmax MOHC-HadGEM2-ES_CLMcom-ETH-COSMO-crCLIM-v1-1 16.52 14.56 -0.37
WWTRES tasmax MOHC-HadGEM2-ES_DMI-HIRHAM5 16.45 14.50 -0.33
WWTRES tasmax MOHC-HadGEM2-ES_GERICS-REMO2015 15.43 13.31 -0.30
WWTRES tasmax MOHC-HadGEM2-ES_KNMI-RACMO22E 15.65 13.50 -0.87
WWTRES tasmax MOHC-HadGEM2-ES_MOHC-HadREM3-GA7-05 15.42 13.39 -0.27
WWTRES tasmax MOHC-HadGEM2-ES_SMHI-RCA4 15.33 13.24 -0.33
WWTRES tasmax MOHC-HadGEM2-ES_UHOH-WRF361H 15.63 13.48 NA
WWTRES tasmin MIROC-MIROC5_CLMcom-CCLM4-8-17 3.12 2.36 0.88
WWTRES tasmin MIROC-MIROC5_GERICS-REMO2015 2.44 1.89 0.90
WWTRES tasmax MIROC-MIROC5_CLMcom-CCLM4-8-17 4.07 3.08 0.84
WWTRES tasmax MIROC-MIROC5_GERICS-REMO2015 4.05 3.32 0.79
WWTRES tasmin ICHEC-EC-EARTH_CLMcom-ETH-COSMO-crCLIM-v1-1 2.18 1.69 0.91
WWTRES tasmin ICHEC-EC-EARTH_DMI-HIRHAM5 2.19 1.66 0.86
WWTRES tasmin ICHEC-EC-EARTH_KNMI-RACMO22E 2.38 1.83 0.87
WWTRES tasmin ICHEC-EC-EARTH_SMHI-RCA4 2.84 2.35 0.70
WWTRES tasmax ICHEC-EC-EARTH_CLMcom-ETH-COSMO-crCLIM-v1-1 3.88 3.17 0.81
WWTRES tasmax ICHEC-EC-EARTH_DMI-HIRHAM5 3.09 2.51 0.86
WWTRES tasmax ICHEC-EC-EARTH_KNMI-RACMO22E 2.96 2.31 0.90
WWTRES tasmax ICHEC-EC-EARTH_SMHI-RCA4 6.08 5.24 0.65
WWTRES tasmin CCCma-CanESM2_CLMcom-CCLM4-8-17 2.84 2.18 0.91
WWTRES tasmin CCCma-CanESM2_GERICS-REMO2015 2.17 1.65 0.87
WWTRES tasmax CCCma-CanESM2_CLMcom-CCLM4-8-17 3.85 2.93 0.84
WWTRES tasmax CCCma-CanESM2_GERICS-REMO2015 4.50 3.70 0.78

Considering only the model combinations with RMSE lower or equal than 4 degrees Celsius and KGE greater than 0.8:

station variable model RMSE MAE KGE
WWRLUN tasmin observation 0.00 0.00 1.00
WWRLUN tasmin MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 2.65 2.06 0.91
WWRLUN tasmin MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.50 1.97 0.88
WWRLUN tasmin MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.42 1.89 0.90
WWRLUN tasmax observation 0.00 0.00 1.00
WWRLUN tasmax MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 3.18 2.52 0.86
WWRLUN tasmax MPI-M-MPI-ESM-LR_DMI-HIRHAM5 3.41 2.71 0.81
WWRLUN tasmax MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 3.24 2.58 0.87
WWRLUN tasmin ICHEC-EC-EARTH_DMI-HIRHAM5 2.61 2.02 0.83
WWRLUN tasmax ICHEC-EC-EARTH_DMI-HIRHAM5 2.79 2.15 0.87
WWRLUN tasmin CCCma-CanESM2_CLMcom-CCLM4-8-17 2.93 2.32 0.90
WWRLUN tasmax CCCma-CanESM2_CLMcom-CCLM4-8-17 3.30 2.64 0.90
WWTRES tasmin observation 0.00 0.00 1.00
WWTRES tasmin MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 2.26 1.74 0.91
WWTRES tasmin MPI-M-MPI-ESM-LR_DMI-HIRHAM5 2.16 1.70 0.90
WWTRES tasmin MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 2.14 1.66 0.93
WWTRES tasmax observation 0.00 0.00 1.00
WWTRES tasmax MPI-M-MPI-ESM-LR_CLMcom-ETH-COSMO-crCLIM-v1-1 3.91 3.13 0.80
WWTRES tasmax MPI-M-MPI-ESM-LR_DMI-HIRHAM5 3.44 2.77 0.81
WWTRES tasmax MPI-M-MPI-ESM-LR_MOHC-HadREM3-GA7-05 3.54 2.82 0.83
WWTRES tasmin ICHEC-EC-EARTH_DMI-HIRHAM5 2.19 1.66 0.86
WWTRES tasmax ICHEC-EC-EARTH_DMI-HIRHAM5 3.09 2.51 0.86
WWTRES tasmin CCCma-CanESM2_CLMcom-CCLM4-8-17 2.84 2.18 0.91
WWTRES tasmax CCCma-CanESM2_CLMcom-CCLM4-8-17 3.85 2.93 0.84

Averaged daily precipitation

Whereas temperature is increasing with a significant trend, the behavior of precipitation due to intermittency and discontuinity is more difficult to analyze. Some recents studies applied to Mediterreanares shows a slighly decrease (e.g. Mascaro, Viola, and Deidda (2018)) with an impact of water resources, especially snow (e.g. Senatore et al. (2022)). AS concern the site WWTRES and WWRLUN monthly time series is presented in the following.

References

Christensen, Ole B., and Erik Kjellström. 2020. “Partitioning Uncertainty Components of Mean Climate and Climate Change in a Large Ensemble of European Regional Climate Model Projections.” Climate Dynamics 54 (9): 4293–4308. https://doi.org/10.1007/s00382-020-05229-y.
Coppola, Erika, Rita Nogherotto, James M. Ciarlo’, Filippo Giorgi, Erik van Meijgaard, Nikolay Kadygrov, Carley Iles, et al. 2021. “Assessment of the European Climate Projections as Simulated by the Large EURO-CORDEX Regional and Global Climate Model Ensemble.” Journal of Geophysical Research: Atmospheres 126 (4): e2019JD032356. https://doi.org/https://doi.org/10.1029/2019JD032356.
E., Cordano, and Eccel E. 2016. “Tools for Stochastic Weather Series Generation in r Environment.” Italian Journal of Agrometeorology 3. https://doi.org/10.19199/2016.3.2038-5625.031.
Giorgetta, Marco A., Johann Jungclaus, Christian H. Reick, Stephanie Legutke, Jürgen Bader, Michael Böttinger, Victor Brovkin, et al. 2013. “Climate and Carbon Cycle Changes from 1850 to 2100 in MPI-ESM Simulations for the Coupled Model Intercomparison Project Phase 5.” Journal of Advances in Modeling Earth Systems 5 (3): 572–97. https://doi.org/https://doi.org/10.1002/jame.20038.
Hazeleger, W., X. Wang, C. Severijns, S. Ştefănescu, R. Bintanja, A. Sterl, K. Wyser, et al. 2012. “EC-Earth V2.2: Description and Validation of a New Seamless Earth System Prediction Model.” Climate Dynamics 39 (11): 2611–29. https://doi.org/10.1007/s00382-011-1228-5.
Martin, The HadGEM2 Development Team: G. M., N. Bellouin, W. J. Collins, I. D. Culverwell, P. R. Halloran, S. C. Hardiman, T. J. Hinton, et al. 2011. “The HadGEM2 Family of Met Office Unified Model Climate Configurations.” Geoscientific Model Development 4 (3): 723–57. https://doi.org/10.5194/gmd-4-723-2011.
Mascaro, Giuseppe, Francesco Viola, and Roberto Deidda. 2018. “Evaluation of Precipitation from EURO-CORDEX Regional Climate Simulations in a Small-Scale Mediterranean Site.” Journal of Geophysical Research: Atmospheres 123 (3): 1604–25. https://doi.org/https://doi.org/10.1002/2017JD027463.
Mauricio Zambrano-Bigiarini. 2020. hydroGOF: Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series. https://doi.org/10.5281/zenodo.839854.
Scinocca, J. F., V. V. Kharin, Y. Jiao, M. W. Qian, M. Lazare, L. Solheim, G. M. Flato, S. Biner, M. Desgagne, and B. Dugas. 2016. “Coordinated Global and Regional Climate Modeling.” Journal of Climate 29 (1): 17–35. https://doi.org/10.1175/JCLI-D-15-0161.1.
Senatore, Alfonso, Domenico Fuoco, Mario Maiolo, Giuseppe Mendicino, Gerhard Smiatek, and Harald Kunstmann. 2022. “Evaluating the Uncertainty of Climate Model Structure and Bias Correction on the Hydrological Impact of Projected Climate Change in a Mediterranean Catchment.” Journal of Hydrology: Regional Studies 42: 101120. https://doi.org/https://doi.org/10.1016/j.ejrh.2022.101120.
Swart, N. C., J. N. S. Cole, V. V. Kharin, M. Lazare, J. F. Scinocca, N. P. Gillett, J. Anstey, et al. 2019. “The Canadian Earth System Model Version 5 (CanESM5.0.3).” Geoscientific Model Development 12 (11): 4823–73. https://doi.org/10.5194/gmd-12-4823-2019.
Vautard, Robert, Nikolay Kadygrov, Carley Iles, Fredrik Boberg, Erasmo Buonomo, Katharina Bülow, Erika Coppola, et al. 2021. “Evaluation of the Large EURO-CORDEX Regional Climate Model Ensemble.” Journal of Geophysical Research: Atmospheres 126 (17): e2019JD032344. https://doi.org/https://doi.org/10.1029/2019JD032344.
Watanabe, Masahiro, Tatsuo Suzuki, Ryouta O’ishi, Yoshiki Komuro, Shingo Watanabe, Seita Emori, Toshihiko Takemura, et al. 2010. “Improved Climate Simulation by Miroc5: Mean States, Variability, and Climate Sensitivity.” Journal of Climate 23 (23): 6312–35. https://doi.org/10.1175/2010JCLI3679.1.